BACKGROUND: One of the ten advanced lung cancer patients presents with poor eastern cooperative oncology group performance status (ECOG PS). There are no clear guidelines about management of these patients. The benefit of tyrosine kinase inhibitors (TKI) in this patient population remains questionable. Hence, in this study, we attempted to develop and validate a predictive score which would predict benefit from oral TKI. METHODS: This was a prospective observational study done at Tata Memorial Hospital, India. Patients with nonsmall cell lung cancer with ECOG PS 3–4 were included in this study. All these patients had received oral TKI on compassionate grounds and were followed up till death. The overall survival (OS) was calculated from date of start of TKI to date of death. R software was used for development and validation of the predictive model. RESULTS: The median survival duration of the discovery cohort and validation cohort were 170.5 and 115 days, respectively. The model predicted OS accurately, within ±2 months in 72.1% and within ±3 months in 81.7% of patients. CONCLUSION: The current model can predict OS in poor PS patients treated with TKI within a satisfactory clinical range and can be used for decision-making of these patients.

Lung cancer is one of the most common cancers.[1] Unfortunately, 9%–11% of nonsmall cell lung cancer (NSCLC) patients present in poor eastern cooperative oncology group (ECOG) performance status (PS 2–4).[2],[3],[4] The cause of poor PS in these patients may be the disease itself, uncontrolled comorbidities, or deterioration in nutritional status (cachexia). Certain causes of poor PS such as pleural effusion, superior vena cava obstruction (SVCO), or uncontrolled comorbidities can be rectified, and chemotherapy can be administered. However, the rest of patients are often offered best supportive care. The median overall survival (OS) in these patients is only 3–4 months.[5],[6]

Use of these oral tyrosine kinase inhibitors (TKI) in patients with PS 3–4 status seems a logical option. In a single arm study reported by Lee et al., NSCLC patients unsuitable for chemotherapy were treated with erlotinib. However, the OS was unsatisfactory.[6] Similar results were published from a randomized study by Goss et al., where the use of gefitinib in patients with poor PS failed to improve OS against placebo.[7] In another randomized study, unselected PS 2–3 NSCLC patients were treated with either erlotinib or placebo. Similar results with respect to OS were seen as with the previous studies. However, a subset of patients who developed rash within 1 month of the start of erlotinib had improvement in OS. The authors hence concluded that erlotinib could be considered as an option in such patients. Interestingly, patients who failed to develop a rash within 1 month had a negative impact on OS with the use of erlotinib.[5] Thus, the use of oral benefits a proportion of patients, but carries the risk of harming others. Selection of patients who are likely to benefit is the need of the hour in NSCLC patients with poor PS. This study was planned to address the above-mentioned lacunae in clinical practice. In this study, we attempted to develop and validate the individual level predictive scores which would identify patients who are unlikely to benefit from oral TKI.

Methods

Study population

The study population had 2 sets. The first set was the discovery cohort of 330 patients and the second set was the validation cohort of 234 patients. Both the set of patients were extracted from an IEC-approved, prospective lung cancer database. The initial 330 patients were selected and subjected to the below-mentioned selection criteria in the period between January 2012 and December 2013. The second set of patients were also selected and subjected to the same selection criteria between January 2014 and October 2015.

Eligibility criteria

The patients were selected if they had Stage IIIB-IV, pathologically proven, previously untreated NSCLC with an ECOG PS 2–4. Patients who had rectifiable poor PS like those with pleural effusion, SVCO or uncontrolled comorbidities or who had deranged liver function tests (defined as serum bilirubin level ≥5 times institutional upper limit and or serum aspartate transaminase/alanine transaminase ≥5 times institutional upper limit) were excluded from the study.

Intervention

These patients with poor ECOG PS were then explained about the disease status and poor prognosis. Options of best supportive care versus treatment with oral TKI were provided. Patients who opted for TKI were offered gefitinib. Poststart of gefitinib patients were followed up on D7 for assessing treatment tolerance and then subsequently at 2 monthly intervals. The death/censored status data of the patients were finally updated on March 22, 2016, for the present study.

The model was developed in 330 patients. Initially, univariate Cox proportional hazard (CPH) regression model was used to detect variables significantly affecting OS. Variables with P > 0.10 were excluded from the study. The variables were selected through a combination of clinical relevance and forward selection method. A total of nine variables from the entire data set were selected to prepare the predictive model for prediction of OS. A sequential series of models were developed with multiple permutation and combination of variables. The multivariate CPHs regression model with the goodness of fit was adopted for the development of models. The best model was selected based on the minimum value of Akaike information criterion. The equation for the best model for prediction of OS is shown in the supplementary appendix.

Statistical validation

The risk model obtained through the development cohort was tested in the validating cohort of 234 patients. A total of 147 had died at the time of analysis. In 5 patients, data regarding death was missing, and hence, OS analysis was restricted to 229 patients. In addition, some variables values were missing. The maximum missing values were 8% for the variable “tobacco pack years.” Since the proportion of missing values were less; no special statistical technique was adopted to handle with missing values. The CPH assumption was tested and found suitable to carry this analysis. A simulation of 20000 burns with 100 refreshments was carried out in open source software “OPENBUGS” (www.openbugs.net). The Markov chain Monte Carlo was considered to run and generate simulation result for posterior predictive means. For each variable, trace plots with 20,000 burns were obtained and they confirmed the convergence for each regression coefficients. The regression coefficients and the corresponding hazard ratio were calculated with exp (regression coefficient) for each variable.

Prediction model performance

A series of methods were evaluated for the performance of the model in the validating cohort. The ability of a model was classified into two outcomes (death or not). The calibration procedure was used to check how closely the predicted probabilities generated numerically (generated OS) with the observed outcomes (observed OS). Accuracy plot was plotted for the predictive survival (generated OS) and actual survival (observed OS) in the validation cohort.

The predictability of the model for depicting death as an event within 6 months was studied. The sensitivity, specificity, and negative predictive value for predicting death at or before 6 months was calculated. In addition, a scatter plot was generated. The Y axis of the plot depicted the difference between the actual and predicted survival and the X axis represented each patient. A Bland–Altman analysis was performed to study the applicability of the equation.

Results

Baseline details

The developmental cohort had 330 patients whereas validating cohort had 234 patients. The median age of the patients was 61 years (interquartile range [IQR] 53–68 years) in the developmental cohort and 60 years (IQR 51–68 years) in validating cohort. The ECOG PS was 4 in 11.2% (n = 37) in developmental cohort and in 15.4% (n = 37) in validating cohort. There were 36.7% patients with medical comorbidities in the developmental cohort as against 37.4% in the validation cohort. The type of comorbidities and its proportion were similar in both cohorts. [Table 1] gives the detail baseline characteristics.{Table 1}

Selection of variables for equation

The variables selected through univariate CPH analysis are shown in [Table 2]. The estimates used to generate the equation for prediction of scores are shown in [Table 3]. The exact equation used to generate predicted score is shown in supplementary appendix 1. The corresponding P value of each variables is listed in [Table 2].{Table 2}{Table 3}

Prediction ability of the equation

Death within 6 months

The sensitivity and specificity of the equation to predict death were 75.8% and 72.5%, respectively. The positive and negative predictive values of the equation to predict death were 75.2% and 73.1%, respectively. The accuracy of the equation for prediction of death was 74.2%. The F1 score for prediction for death was 75.5%.

Prediction of exact survival

The median survival duration for the cohort of 330 and 229 patients were 170.5 and 115 days, respectively. The model predicted OS accuracy within ±2 months in 72.1% and within ±3 months in 81.7% of patients. [Figure 1] depicts the plot of the difference between predicted and actual survival. [Figure 2] depicts accuracy plot of the predicted survival with actual survival in accordance with each patient. Bland–Altman plot was generated to see the prediction accuracy of predicted survival [Figure 3]. The prediction accuracy was 94.8% (n = 217 patients). The curve of actual and predicted survival distribution is shown in [Figure 4]. The curves can be visualized to be overlapping and crisscrossing.{Figure 1}{Figure 2}{Figure 3}{Figure 4}

Discussion

Personalized or individualized treatment in cancers is associated with improvement in outcomes. Use of clinical scores in oncology for decision-making is one of the methods of individualization of treatment. Scores have been used for selection of treatment in renal cell carcinomas, for the decision of deep venous thrombosis prophylaxis and for prediction of survival in chronic myeloid leukemia. Treatment predicaments were resolved by use of these scores.[8],[9],[10] Treatment of poor PS lung cancers patients is an unresolved dilemma at present. Randomized and single arm studies done in this situation with oral TKI (Gefitinib or erlotinib) have failed to improve the OS in comparison with placebo. Hence, the NCCN guidelines provide the option of best supportive care in this situation. However, a proportion of patients do benefit from this therapy and proportion are harmed too. In the TOPICAL study, the authors stated that patients who had rash within 1 month of erlotinib benefitted from the treatment.[5] However, using rash as a parameter, puts the patient at risk to a potentially harmful treatment, to select the proportion of patients who are likely to benefit. To avoid this harm, we need to avoid treatment in patients who will not benefit from such treatment. To do so, we need a predictive score that can be done at baseline itself. The current equation provides it.

The current equation has been generated through robust methodology and internal validation. The predicted survivals in the validation cohort concurred with the actual survivals within a range of ±2 months in 72.1% and within ±3 months in 81.7% of patients. The positive predictive value for identifying a patient who will not survive above 6 months with the model was 75.2%. The predicted death within 6 months should be considered when deciding whether TKI needs to be administered or not. The median survival in randomized studies in poor PS NSCLC was 3–4 months. Hence, a predicted death within 6 months would identify patients who would not benefit from TKI [Table 4].{Table 4}

The current analysis has its own strengths and limitations. The patient details are derived from a consecutive prospective database, and the model reflects its ability for prediction in routine day to day practice. Although patients selected in this study had PS 3–4 at baseline, an attempt was made to get pathological proof of malignancy in all patients and only patients who had pathologically proven NSCLC were selected for this study. Thus, the study excluded the probability of having a benign disease like tuberculosis that mimics malignancy, being included in the analysis. The limitation of the study was that it was a single center study and that it was a post hoc analysis. However, the variables utilized in the study were captured prospectively for the primary protocol, and it is unlikely that the post hoc status would impact the study results.

Conclusion

The current model can predict OS in poor PS patients treated with TKI within satisfactory clinical range and can be used for a decision on treatment of these patients.